1. Field of the Invention
The present invention generally relates to image segmentation, and more particularly to extraction of a perceptual feature set for image/video segmentation.
2. Description of the Prior Art
Image segmentation is a type of image analysis operation that breaks an image into individual objects or regions by highlighting or isolating individual objects within the image. Image segmentation can be useful in locating objects and boundaries within an image or video in applications such as computer vision. Intensity and/or texture information is commonly used to perform segmentation on gray-scale images.
Color information and texture information are used in color image segmentation. A proper feature set for describing the colors and textures within the image is essential for meaningful image/video segmentation that approximates human visual interpretation or perception. Textural features are commonly described by statistical methods and spectral methods. Common statistical methods are Kth order moment, uniformity, entropy, and co-occurrence matrix. Common spectral methods are Laws filter, discrete cosine transform (DCT), Fourier domain analysis with ring and wedge filter, Gabor filter bank, and wavelength transform. Color features, on the other hand, are commonly described using a variety of color spaces, such as RGB (red/green/blue), YUV, CIELAB and HSI (hue/saturation/intensity). As the HSI color space is very close to that for human interpretation of color, it is commonly used in various applications of computer vision. However, the color textures are usually difficult to describe using color features, textural features or their combination. Better color/texture features usually have high dimensionality and thus complicate the subsequent processing in the segmentation operation.
For the reason that conventional color/texture features could not be effectively used to describe colors and textures within the image, a need has thus arisen to propose a novel feature set in describing colors and textures, making the overall segmentation results close to human interpretation.